// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
#define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H

namespace Eigen {

/** \class TensorImagePatch
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Patch extraction specialized for image processing.
  * This assumes that the input has a least 3 dimensions ordered as follow:
  *  1st dimension: channels (of size d)
  *  2nd dimension: rows (of size r)
  *  3rd dimension: columns (of size c)
  *  There can be additional dimensions such as time (for video) or batch (for
  * bulk processing after the first 3.
  * Calling the image patch code with patch_rows and patch_cols is equivalent
  * to calling the regular patch extraction code with parameters d, patch_rows,
  * patch_cols, and 1 for all the additional dimensions.
  */
namespace internal {

    template <DenseIndex Rows, DenseIndex Cols, typename XprType> struct traits<TensorImagePatchOp<Rows, Cols, XprType>> : public traits<XprType>
    {
        typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
        typedef traits<XprType> XprTraits;
        typedef typename XprTraits::StorageKind StorageKind;
        typedef typename XprTraits::Index Index;
        typedef typename XprType::Nested Nested;
        typedef typename remove_reference<Nested>::type _Nested;
        static const int NumDimensions = XprTraits::NumDimensions + 1;
        static const int Layout = XprTraits::Layout;
        typedef typename XprTraits::PointerType PointerType;
    };

    template <DenseIndex Rows, DenseIndex Cols, typename XprType> struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense>
    {
        typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
    };

    template <DenseIndex Rows, DenseIndex Cols, typename XprType>
    struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType>>::type>
    {
        typedef TensorImagePatchOp<Rows, Cols, XprType> type;
    };

    template <typename Self, bool Vectorizable> struct ImagePatchCopyOp
    {
        typedef typename Self::Index Index;
        typedef typename Self::Scalar Scalar;
        typedef typename Self::Impl Impl;
        static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
        Run(const Self& self, const Index num_coeff_to_copy, const Index dst_index, Scalar* dst_data, const Index src_index)
        {
            const Impl& impl = self.impl();
            for (Index i = 0; i < num_coeff_to_copy; ++i) { dst_data[dst_index + i] = impl.coeff(src_index + i); }
        }
    };

    template <typename Self> struct ImagePatchCopyOp<Self, true>
    {
        typedef typename Self::Index Index;
        typedef typename Self::Scalar Scalar;
        typedef typename Self::Impl Impl;
        typedef typename packet_traits<Scalar>::type Packet;
        static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
        Run(const Self& self, const Index num_coeff_to_copy, const Index dst_index, Scalar* dst_data, const Index src_index)
        {
            const Impl& impl = self.impl();
            const Index packet_size = internal::unpacket_traits<Packet>::size;
            const Index vectorized_size = (num_coeff_to_copy / packet_size) * packet_size;
            for (Index i = 0; i < vectorized_size; i += packet_size)
            {
                Packet p = impl.template packet<Unaligned>(src_index + i);
                internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, p);
            }
            for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) { dst_data[dst_index + i] = impl.coeff(src_index + i); }
        }
    };

    template <typename Self> struct ImagePatchPaddingOp
    {
        typedef typename Self::Index Index;
        typedef typename Self::Scalar Scalar;
        typedef typename packet_traits<Scalar>::type Packet;
        static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Index num_coeff_to_pad, const Scalar padding_value, const Index dst_index, Scalar* dst_data)
        {
            const Index packet_size = internal::unpacket_traits<Packet>::size;
            const Packet padded_packet = internal::pset1<Packet>(padding_value);
            const Index vectorized_size = (num_coeff_to_pad / packet_size) * packet_size;
            for (Index i = 0; i < vectorized_size; i += packet_size) { internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, padded_packet); }
            for (Index i = vectorized_size; i < num_coeff_to_pad; ++i) { dst_data[dst_index + i] = padding_value; }
        }
    };

}  // end namespace internal

template <DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors>
{
public:
    typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested;
    typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
    typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr,
                                                             DenseIndex patch_rows,
                                                             DenseIndex patch_cols,
                                                             DenseIndex row_strides,
                                                             DenseIndex col_strides,
                                                             DenseIndex in_row_strides,
                                                             DenseIndex in_col_strides,
                                                             DenseIndex row_inflate_strides,
                                                             DenseIndex col_inflate_strides,
                                                             PaddingType padding_type,
                                                             Scalar padding_value)
        : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols), m_row_strides(row_strides), m_col_strides(col_strides),
          m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides), m_row_inflate_strides(row_inflate_strides),
          m_col_inflate_strides(col_inflate_strides), m_padding_explicit(false), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
          m_padding_type(padding_type), m_padding_value(padding_value)
    {
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr,
                                                             DenseIndex patch_rows,
                                                             DenseIndex patch_cols,
                                                             DenseIndex row_strides,
                                                             DenseIndex col_strides,
                                                             DenseIndex in_row_strides,
                                                             DenseIndex in_col_strides,
                                                             DenseIndex row_inflate_strides,
                                                             DenseIndex col_inflate_strides,
                                                             DenseIndex padding_top,
                                                             DenseIndex padding_bottom,
                                                             DenseIndex padding_left,
                                                             DenseIndex padding_right,
                                                             Scalar padding_value)
        : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols), m_row_strides(row_strides), m_col_strides(col_strides),
          m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides), m_row_inflate_strides(row_inflate_strides),
          m_col_inflate_strides(col_inflate_strides), m_padding_explicit(true), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
          m_padding_left(padding_left), m_padding_right(padding_right), m_padding_type(PADDING_VALID), m_padding_value(padding_value)
    {
    }

    EIGEN_DEVICE_FUNC
    DenseIndex patch_rows() const { return m_patch_rows; }
    EIGEN_DEVICE_FUNC
    DenseIndex patch_cols() const { return m_patch_cols; }
    EIGEN_DEVICE_FUNC
    DenseIndex row_strides() const { return m_row_strides; }
    EIGEN_DEVICE_FUNC
    DenseIndex col_strides() const { return m_col_strides; }
    EIGEN_DEVICE_FUNC
    DenseIndex in_row_strides() const { return m_in_row_strides; }
    EIGEN_DEVICE_FUNC
    DenseIndex in_col_strides() const { return m_in_col_strides; }
    EIGEN_DEVICE_FUNC
    DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
    EIGEN_DEVICE_FUNC
    DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
    EIGEN_DEVICE_FUNC
    bool padding_explicit() const { return m_padding_explicit; }
    EIGEN_DEVICE_FUNC
    DenseIndex padding_top() const { return m_padding_top; }
    EIGEN_DEVICE_FUNC
    DenseIndex padding_bottom() const { return m_padding_bottom; }
    EIGEN_DEVICE_FUNC
    DenseIndex padding_left() const { return m_padding_left; }
    EIGEN_DEVICE_FUNC
    DenseIndex padding_right() const { return m_padding_right; }
    EIGEN_DEVICE_FUNC
    PaddingType padding_type() const { return m_padding_type; }
    EIGEN_DEVICE_FUNC
    Scalar padding_value() const { return m_padding_value; }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }

protected:
    typename XprType::Nested m_xpr;
    const DenseIndex m_patch_rows;
    const DenseIndex m_patch_cols;
    const DenseIndex m_row_strides;
    const DenseIndex m_col_strides;
    const DenseIndex m_in_row_strides;
    const DenseIndex m_in_col_strides;
    const DenseIndex m_row_inflate_strides;
    const DenseIndex m_col_inflate_strides;
    const bool m_padding_explicit;
    const DenseIndex m_padding_top;
    const DenseIndex m_padding_bottom;
    const DenseIndex m_padding_left;
    const DenseIndex m_padding_right;
    const PaddingType m_padding_type;
    const Scalar m_padding_value;
};

// Eval as rvalue
template <DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device> struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
{
    typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType;
    typedef typename XprType::Index Index;
    static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
    static const int NumDims = NumInputDims + 1;
    typedef DSizes<Index, NumDims> Dimensions;
    typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
    typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device> Self;
    typedef TensorEvaluator<ArgType, Device> Impl;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    enum
    {
        IsAligned = false,
        PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
        BlockAccess = false,
        PreferBlockAccess = true,
        Layout = TensorEvaluator<ArgType, Device>::Layout,
        CoordAccess = false,
        RawAccess = false
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_device(device), m_impl(op.expression(), device)
    {
        EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE);

        m_paddingValue = op.padding_value();

        const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();

        // Caches a few variables.
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            m_inputDepth = input_dims[0];
            m_inputRows = input_dims[1];
            m_inputCols = input_dims[2];
        }
        else
        {
            m_inputDepth = input_dims[NumInputDims - 1];
            m_inputRows = input_dims[NumInputDims - 2];
            m_inputCols = input_dims[NumInputDims - 3];
        }

        m_row_strides = op.row_strides();
        m_col_strides = op.col_strides();

        // Input strides and effective input/patch size
        m_in_row_strides = op.in_row_strides();
        m_in_col_strides = op.in_col_strides();
        m_row_inflate_strides = op.row_inflate_strides();
        m_col_inflate_strides = op.col_inflate_strides();
        // The "effective" input rows and input cols are the input rows and cols
        // after inflating them with zeros.
        // For examples, a 2x3 matrix with row_inflate_strides and
        // col_inflate_strides of 2 comes from:
        //   A B C
        //   D E F
        //
        // to a matrix is 3 x 5:
        //
        //   A . B . C
        //   . . . . .
        //   D . E . F

        m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
        m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
        m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
        m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);

        if (op.padding_explicit())
        {
            m_outputRows =
                numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
            m_outputCols =
                numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
            m_rowPaddingTop = op.padding_top();
            m_colPaddingLeft = op.padding_left();
        }
        else
        {
            // Computing padding from the type
            switch (op.padding_type())
            {
            case PADDING_VALID:
                m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
                m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
                // Calculate the padding
                m_rowPaddingTop = numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2);
                m_colPaddingLeft = numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2);
                break;
            case PADDING_SAME:
                m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
                m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
                // Calculate the padding
                m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2;
                m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2;
                // The padding size calculation for PADDING_SAME has been updated to
                // be consistent with how TensorFlow extracts its paddings.
                m_rowPaddingTop = numext::maxi<Index>(0, m_rowPaddingTop);
                m_colPaddingLeft = numext::maxi<Index>(0, m_colPaddingLeft);
                break;
            default:
                eigen_assert(false && "unexpected padding");
                m_outputCols = 0;  // silence the uninitialised warning;
                m_outputRows = 0;  //// silence the uninitialised warning;
            }
        }
        eigen_assert(m_outputRows > 0);
        eigen_assert(m_outputCols > 0);

        // Dimensions for result of extraction.
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            // ColMajor
            // 0: depth
            // 1: patch_rows
            // 2: patch_cols
            // 3: number of patches
            // 4 and beyond: anything else (such as batch).
            m_dimensions[0] = input_dims[0];
            m_dimensions[1] = op.patch_rows();
            m_dimensions[2] = op.patch_cols();
            m_dimensions[3] = m_outputRows * m_outputCols;
            for (int i = 4; i < NumDims; ++i) { m_dimensions[i] = input_dims[i - 1]; }
        }
        else
        {
            // RowMajor
            // NumDims-1: depth
            // NumDims-2: patch_rows
            // NumDims-3: patch_cols
            // NumDims-4: number of patches
            // NumDims-5 and beyond: anything else (such as batch).
            m_dimensions[NumDims - 1] = input_dims[NumInputDims - 1];
            m_dimensions[NumDims - 2] = op.patch_rows();
            m_dimensions[NumDims - 3] = op.patch_cols();
            m_dimensions[NumDims - 4] = m_outputRows * m_outputCols;
            for (int i = NumDims - 5; i >= 0; --i) { m_dimensions[i] = input_dims[i]; }
        }

        // Strides for moving the patch in various dimensions.
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            m_colStride = m_dimensions[1];
            m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
            m_otherStride = m_patchStride * m_dimensions[3];
        }
        else
        {
            m_colStride = m_dimensions[NumDims - 2];
            m_patchStride = m_colStride * m_dimensions[NumDims - 3] * m_dimensions[NumDims - 1];
            m_otherStride = m_patchStride * m_dimensions[NumDims - 4];
        }

        // Strides for navigating through the input tensor.
        m_rowInputStride = m_inputDepth;
        m_colInputStride = m_inputDepth * m_inputRows;
        m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols;

        // Fast representations of different variables.
        m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
        m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
        m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
        m_fastInflateRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
        m_fastInflateColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
        m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);

        // Number of patches in the width dimension.
        m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
        }
        else
        {
            m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims - 1]);
        }
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/)
    {
        m_impl.evalSubExprsIfNeeded(NULL);
        return true;
    }

#ifdef EIGEN_USE_THREADS
    template <typename EvalSubExprsCallback> EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done)
    {
        m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
    }
#endif  // EIGEN_USE_THREADS

    EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
    {
        // Patch index corresponding to the passed in index.
        const Index patchIndex = index / m_fastPatchStride;
        // Find the offset of the element wrt the location of the first element.
        const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;

        // Other ways to index this element.
        const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
        const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;

        // Calculate col index in the input original tensor.
        const Index colIndex = patch2DIndex / m_fastOutputRows;
        const Index colOffset = patchOffset / m_fastColStride;
        const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
        const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0);
        if (inputCol < 0 || inputCol >= m_input_cols_eff || ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides)))
        {
            return Scalar(m_paddingValue);
        }

        // Calculate row index in the original input tensor.
        const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
        const Index rowOffset = patchOffset - colOffset * m_colStride;
        const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
        const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0);
        if (inputRow < 0 || inputRow >= m_input_rows_eff || ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides)))
        {
            return Scalar(m_paddingValue);
        }

        const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
        const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];

        const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride;
        return m_impl.coeff(inputIndex);
    }

    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
        eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());

        if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1)
        {
            return packetWithPossibleZero(index);
        }

        const Index indices[2] = {index, index + PacketSize - 1};
        const Index patchIndex = indices[0] / m_fastPatchStride;
        if (patchIndex != indices[1] / m_fastPatchStride)
        {
            return packetWithPossibleZero(index);
        }
        const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride;
        eigen_assert(otherIndex == indices[1] / m_fastOtherStride);

        // Find the offset of the element wrt the location of the first element.
        const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
                                       (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};

        const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
        eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);

        const Index colIndex = patch2DIndex / m_fastOutputRows;
        const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};

        // Calculate col indices in the original input tensor.
        const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] - m_colPaddingLeft, colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
        if (inputCols[1] < 0 || inputCols[0] >= m_inputCols)
        {
            return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
        }

        if (inputCols[0] == inputCols[1])
        {
            const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
            const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0] * m_colStride, patchOffsets[1] - colOffsets[1] * m_colStride};
            eigen_assert(rowOffsets[0] <= rowOffsets[1]);
            // Calculate col indices in the original input tensor.
            const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] - m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};

            if (inputRows[1] < 0 || inputRows[0] >= m_inputRows)
            {
                return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
            }

            if (inputRows[0] >= 0 && inputRows[1] < m_inputRows)
            {
                // no padding
                const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
                const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
                const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
                return m_impl.template packet<Unaligned>(inputIndex);
            }
        }

        return packetWithPossibleZero(index);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_impl.bind(cgh); }
#endif

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputRows() const { return m_outputRows; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputCols() const { return m_outputCols; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userRowStride() const { return m_row_strides; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userColStride() const { return m_col_strides; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInRowStride() const { return m_in_row_strides; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInColStride() const { return m_in_col_strides; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        // We conservatively estimate the cost for the code path where the computed
        // index is inside the original image and
        // TensorEvaluator<ArgType, Device>::CoordAccess is false.
        const double compute_cost = 3 * TensorOpCost::DivCost<Index>() + 6 * TensorOpCost::MulCost<Index>() + 8 * TensorOpCost::MulCost<Index>();
        return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
    }

protected:
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
    {
        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
        EIGEN_UNROLL_LOOP
        for (int i = 0; i < PacketSize; ++i) { values[i] = coeff(index + i); }
        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
        return rslt;
    }

    Dimensions m_dimensions;

    Index m_otherStride;
    Index m_patchStride;
    Index m_colStride;
    Index m_row_strides;
    Index m_col_strides;

    Index m_in_row_strides;
    Index m_in_col_strides;
    Index m_row_inflate_strides;
    Index m_col_inflate_strides;

    Index m_input_rows_eff;
    Index m_input_cols_eff;
    Index m_patch_rows_eff;
    Index m_patch_cols_eff;

    internal::TensorIntDivisor<Index> m_fastOtherStride;
    internal::TensorIntDivisor<Index> m_fastPatchStride;
    internal::TensorIntDivisor<Index> m_fastColStride;
    internal::TensorIntDivisor<Index> m_fastInflateRowStride;
    internal::TensorIntDivisor<Index> m_fastInflateColStride;
    internal::TensorIntDivisor<Index> m_fastInputColsEff;

    Index m_rowInputStride;
    Index m_colInputStride;
    Index m_patchInputStride;

    Index m_inputDepth;
    Index m_inputRows;
    Index m_inputCols;

    Index m_outputRows;
    Index m_outputCols;

    Index m_rowPaddingTop;
    Index m_colPaddingLeft;

    internal::TensorIntDivisor<Index> m_fastOutputRows;
    internal::TensorIntDivisor<Index> m_fastOutputDepth;

    Scalar m_paddingValue;

    const Device EIGEN_DEVICE_REF m_device;
    TensorEvaluator<ArgType, Device> m_impl;
};

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
